![]() ![]() Outperforms previous models in terms of audio quality. Experimental results demonstrate that our model Learning framework, HiddenSinger-U, to train the model using an unlabeled InĪddition, our proposed model is extended to an unsupervised singing voice Subsequently, we use the latentĭiffusion models to sample a latent representation from a musical score. Low-dimensional compressed latent vector. We introduce an audio autoencoder that can encode audio into an audio codec asĪ compressed representation and reconstruct the high-fidelity audio from the Neural audio codec and latent diffusion models. ![]() We propose HiddenSinger, a high-quality singing voice synthesis system using a ToĪlleviate the challenges posed by model complexity in singing voice synthesis, Requires very high-dimensional samples with long-term acoustic features. Limitations in terms of complexity and controllability, as speech synthesis However, in the speech domain, theĪpplication of diffusion models for synthesizing time-varying audio faces Download a PDF of the paper titled HiddenSinger: High-Quality Singing Voice Synthesis via Neural Audio Codec and Latent Diffusion Models, by Ji-Sang Hwang and 2 other authors Download PDF Abstract: Recently, denoising diffusion models have demonstrated remarkable performanceĪmong generative models in various domains. ![]()
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